Sorin Mihai Grigorescu

A bci-controlled robotic assistant for quadriplegic people in domestic and professional life

In this paper, a Brain-Computer Interface (BCI) control approach for the assistive robotic system FRIEND is presented. The objective of the robot is to assist elderly and persons with disabilities in their daily and professional life activities. FRIEND is presented here from an architectural point of view, that is, as an overall robotic device that includes many subareas of research, such as human-robot interaction, perception, object manipulation and path planning, robotic safety, and so on. The integration of the hardware and software components is described relative to the interconnections between the various elements of FRIEND and the approach used for human-machine interaction. Since the robotic system is intended to be used especially by patients suffering from a high degree of disability (e.g., patients which are quadriplegic, have muscle diseases or serious paralysis due to strokes, or any other diseases with similar consequences for their independence), an alternative non-invasive BCI has been investigated. The FRIEND-BCI paradigm is explained within the overall structure of the robot. The capabilities of the robotic system are demonstrated in three support scenarios, one that deals with Activities of daily living (ADL) and two that are taking place in a rehabilitation workshop. The proposed robot was clinically evaluated through different tests that directly measure task execution time and hardware performance, as well as the acceptance of robot by end-users.

Robust camera pose and scene structure analysis for service robotics

Successful path planning and object manipulation in service robotics applications rely both on a good estimation of the robots position and orientation (pose) in the environment, as well as on a reliable understanding of the visualized scene. In this paper a robust real-time camera pose and a scene structure estimation system is proposed. First, the pose of the camera is estimated through the analysis of the so-called tracks. The tracks include key features from the imaged scene and geometric constraints which are used to solve the pose estimation problem. Second, based on the calculated pose of the camera, i.e. robot, the scene is analyzed via a robust depth segmentation and object classification approach. In order to reliably segment the objects depth, a feedback control technique at an image processing level has been used with the purpose of improving the robustness of the robotic vision system with respect to external influences, such as cluttered scenes and variable illumination conditions. The control strategy detailed in this paper is based on the traditional open-loop mathematical model of the depth estimation process. In order to control a robotic system, the obtained visual information is classified into objects of interest and obstacles. The proposed scene analysis architecture is evaluated through experimental results within a robotic collision avoidance system.

ROVIS: Robust machine vision for service robotic system FRIEND

In this paper the vision architecture, named ROVIS, of the robotic system FRIEND is presented. The main concept of the ROVIS is the inclusion of feedback structures between different components of the vision system as well as between the vision and other modules of the robotic system to achieve high robustness against external influences of the individual system units as well as of the system as whole. The novelty of this work lies in the inclusion of feedback control at different levels of the 2D object recognition system to provide reliable inputs to the 3D object reconstruction and object manipulation modules of the robotic system FRIEND. The idea behind this approach is to change the processing parameters in a closed-loop manner so that the current image processing result at a particular processing level is driven to a desired result. The effectiveness of the ROVIS system is demonstrated through the presentation of experimental results on 3D reconstruction of different objects from FRIEND environment.

Closed-Loop Control in Image Processing for Improvement of Object Recognition

This paper presents the employment of a novel idea of inclusion of feedback control at different image processing levels to improve the robustness of object recognition. The proposed approach is intended to cope with object image color uncertainty that arises from changes in the illumination conditions during image acquisition. The focus is on the control of quality of binary segmented image, which represents the input to higher processing levels, feature extraction and recognition, of the image processing chain. The main idea behind this is that introduced closed-loops drive the current segmented image to the image of the desired, reference, quality so that higher processing levels are provided with reliable input image data. In this way, the reliability and robustness against external influences of the overall object recognition system is improved. The specifics and benefit of closed-loop control in image processing are considered throughout the presentation of the closed-loop object recognition in the robotic system FRIEND II. The benefit of the closed-loop image segmentation in service robotics is considered through the demonstration of results achieved for recognition of the object of interest from a working scenario of the system FRIEND II.

Multiple-superquadrics based object surface estimation for grasping in service robotics

A method for approximating an objects surface defined by 2.5D data is presented. The goal is to determine a compact volume which may be subjected to manipulation actions under a proper grasp planning. The object volume is obtained based on a union of parameterized geometric shapes, also known as superquadrics. In order to reduce over-segmentations and also the number of fitting combinations, the object space is defined as a 3D bounding Region of Interest (ROI). Further, via voxel decomposition, the ROI is decomposed in more meaningful smaller ROIs that can capture a large number of features from the object of interest. By using a fixed size voxel grid, the process can be slowed down for the cases of large objects. A boosting speedup of the process is proposed through dynamically adjusting the sides of the voxels for each object. Finally, a method for voxels merging is proposed. Each of the newly created ROIs, related to a particular region of the object, will be hosting a superquadric model which optimally estimates the considered surface.

A Time-Delay Control Approach for a Stereo Vision Based Human-Machine Interaction System

In this paper, an approach to control a 6-DoF stereo camera for the purpose of actively tracking the face of a human observer in the context of Human-Robot Interaction (HRI) is proposed. The main objective in the presented work is to cope with the critical time-delay introduced by the computer vision algorithms used to acquire the feedback variable within the control system. In the studied HRI architecture, the feedback variable is represented by the 3D position of a human subject. We proposed a predictive control method which is able to handle the high time-delay inserted by the vision elements into the control system of the stereo camera. Also, along with the predictive control approach, a novel 3D nose detection algorithm is suggested for the computation of the feedback variable. The performance of the implemented platform is given through experimental results.

Robust Stereo-Vision Based 3D Object Reconstruction for the Assistive Robot FRIEND

A key requirement of assistive robot vision is the robust 3D object reconstruction in complex environments for reliable autonomous object manipulation. In this paper the idea is presen ...

Robust feature extraction for 3D reconstruction of boundary segmented objects in a robotic Library scenario

In this paper a vision system for robust feature extraction and 3D reconstruction of boundary segmented objects is presented. The goal of the system is reliable perception of a professional life environment in a scenario of the rehabilitation robot FRIEND. Reconstructed scenes are used to plan object manipulation with a 7-DoF manipulator arm. The robustness of boundary feature extraction is achieved by the means of including feedback control at image segmentation level. The objective of feedback is to adjust the segmentation parameters in order to cope with scene uncertainties, such as variable illumination conditions. Robustly extracted 2D object features are provided as input to the 3D object reconstruction module of the FRIEND vision system. The performance of the proposed approach is evaluated through experiments in the Library scenario of the robotic system FRIEND.

GPRS Based System for Atmospheric Pollution Monitoring and Warning

This paper presents the synthesis of a SCADA (supervisory control and data acquisition) system, named Pollution Guard, designed to collect and process atmospheric pollution data measured in several strategic points of a region. Pollution Guard makes use of the GPRS (general packet radio service) data communication infrastructure from a mobile communication provider that covers a very large area, practically the air pollution data being collected from every place in the country. In comparison to other similar systems, the new functionalities provided by Pollution Guard are the SMS (short messaging system) and e-mail alerts generated when the level of toxic substances exceeds some given values, chosen with regard to respiratory illness

A Combined Experience and Model Based Design Methodology of a Fuzzy Control System for Mean Arterial Pressure and Cardiac Output

Abstract The control of physiological variables presents specific challenges, mainly due to the highly nonlinear, complex behavior of biological systems. Cardiovascular system stands as a clear example, with critical situations when control is desirable, but troublesome in the same time. This paper presents a fuzzy control system for two cardiovascular variables, blood pressure and cardiac output, through automatically infusion of two commonly used drugs, Sodium Nitroprusside and Dopamine, respectively. Simulations are possible, making use of a combined cardiovascular-pharmacological model, describing the effects of drugs’ infusion rates on controlled variables. The fuzzy controllers used are PI type, designed by experience, and further tuned based on the nominal values of parameters of the cardiovascular-pharmacological model. The main goal is achieving the normal values of cardiovascular variables within a reasonable time period.